Conventional indoor navigation techniques include ultrasonic or laser ranging, tracking marked objects with cameras, and interpreting video scenes as captured by a camera. This last method, navigating as a person would by interpreting his visual surroundings, is an outstanding problem in computer vision research.
A variety of challenges are associated with these and other indoor navigation techniques. Occlusion, for example, occurs when a camera or detector's view is blocked. Lack of sensitivity can be an issue when object-tracking cameras are located too close to one another, leading to small angle measurements. Some vision-based navigation systems depend on surface texture which may not always be available in an image. Finally, incremental positioning methods may accumulate errors which degrade positioning accuracy.
Building construction is one scenario in which indoor navigation is a valuable capability. Robots that lay out construction plans or install fixtures need accurate position and orientation information to do their jobs. Assembly of large aircraft parts offers another example. Precisely mating airplane fuselage or wing sections is helped by keeping track of the position and orientation of each component. In scenarios like these, as a practical matter, it is helpful for indoor navigation solutions to be expressed in the same coordinates as locations of building structures such as walls, floors, ceilings, doorways and the like.
Many vision-based indoor navigation systems cannot run in real time because the computational requirements are too great. Finally, a navigation system for a small robot is impractical if it consumes too much power or weighs or costs too much. What is needed is an indoor navigation system that permits accurate tracking of the location and orientation of objects in an indoor space while overcoming the challenges mentioned above and without requiring excessive computational capacity, electrical power or weight.